Overview

Dataset statistics

Number of variables21
Number of observations665414
Missing cells0
Missing cells (%)0.0%
Duplicate rows1739
Duplicate rows (%)0.3%
Total size in memory111.7 MiB
Average record size in memory176.0 B

Variable types

DateTime1
Text3
Categorical1
Numeric16

Alerts

Dataset has 1739 (0.3%) duplicate rowsDuplicates
CO 1st Max Value is highly overall correlated with CO AQI and 4 other fieldsHigh correlation
CO AQI is highly overall correlated with CO 1st Max Value and 4 other fieldsHigh correlation
CO Mean is highly overall correlated with CO 1st Max Value and 4 other fieldsHigh correlation
NO2 1st Max Value is highly overall correlated with CO 1st Max Value and 4 other fieldsHigh correlation
NO2 AQI is highly overall correlated with CO 1st Max Value and 4 other fieldsHigh correlation
NO2 Mean is highly overall correlated with CO 1st Max Value and 4 other fieldsHigh correlation
O3 1st Max Value is highly overall correlated with O3 AQI and 1 other fieldsHigh correlation
O3 AQI is highly overall correlated with O3 1st Max Value and 1 other fieldsHigh correlation
O3 Mean is highly overall correlated with O3 1st Max Value and 1 other fieldsHigh correlation
SO2 1st Max Value is highly overall correlated with SO2 AQI and 1 other fieldsHigh correlation
SO2 AQI is highly overall correlated with SO2 1st Max Value and 1 other fieldsHigh correlation
SO2 Mean is highly overall correlated with SO2 1st Max Value and 1 other fieldsHigh correlation
CO Mean has 21340 (3.2%) zerosZeros
CO 1st Max Value has 22094 (3.3%) zerosZeros
CO 1st Max Hour has 310027 (46.6%) zerosZeros
CO AQI has 23043 (3.5%) zerosZeros
SO2 Mean has 46666 (7.0%) zerosZeros
SO2 1st Max Value has 52679 (7.9%) zerosZeros
SO2 1st Max Hour has 114884 (17.3%) zerosZeros
SO2 AQI has 234459 (35.2%) zerosZeros
NO2 1st Max Hour has 61611 (9.3%) zerosZeros

Reproduction

Analysis started2025-03-29 01:27:12.628239
Analysis finished2025-03-29 01:28:16.973573
Duration1 minute and 4.35 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Date
Date

Distinct8674
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size10.2 MiB
Minimum2000-01-01 00:00:00
Maximum2023-09-30 00:00:00
2025-03-29T01:28:17.030395image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:17.116415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct221
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.2 MiB
2025-03-29T01:28:17.304394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length75
Median length48
Mean length26.922675
Min length6

Characters and Unicode

Total characters17914725
Distinct characters74
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
2nd row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
3rd row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
4th row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
5th row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
ValueCountFrequency (%)
st 159198
 
5.0%
street 85351
 
2.7%
ave 80475
 
2.5%
n 57179
 
1.8%
53322
 
1.7%
blvd 51247
 
1.6%
e 50042
 
1.6%
road 48076
 
1.5%
rd 45138
 
1.4%
s 33180
 
1.0%
Other values (672) 2511380
79.1%
2025-03-29T01:28:17.579912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2563925
 
14.3%
E 999774
 
5.6%
A 929165
 
5.2%
S 826288
 
4.6%
T 822465
 
4.6%
R 722187
 
4.0%
N 659055
 
3.7%
O 605493
 
3.4%
L 568918
 
3.2%
1 469505
 
2.6%
Other values (64) 8747950
48.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 9472102
52.9%
Space Separator 2563925
 
14.3%
Lowercase Letter 2558075
 
14.3%
Decimal Number 2479927
 
13.8%
Other Punctuation 727291
 
4.1%
Dash Punctuation 79112
 
0.4%
Open Punctuation 18595
 
0.1%
Close Punctuation 15698
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 999774
 
10.6%
A 929165
 
9.8%
S 826288
 
8.7%
T 822465
 
8.7%
R 722187
 
7.6%
N 659055
 
7.0%
O 605493
 
6.4%
L 568918
 
6.0%
I 467137
 
4.9%
D 370273
 
3.9%
Other values (15) 2501347
26.4%
Lowercase Letter
ValueCountFrequency (%)
e 397181
15.5%
t 277122
10.8%
n 230246
9.0%
r 227799
8.9%
a 215336
8.4%
i 188227
 
7.4%
o 182257
 
7.1%
l 160097
 
6.3%
s 110647
 
4.3%
d 86925
 
3.4%
Other values (15) 482238
18.9%
Decimal Number
ValueCountFrequency (%)
1 469505
18.9%
0 445153
18.0%
2 305413
12.3%
5 262629
10.6%
3 228815
9.2%
4 191912
7.7%
6 166125
 
6.7%
8 160240
 
6.5%
7 146439
 
5.9%
9 103696
 
4.2%
Other Punctuation
ValueCountFrequency (%)
, 321907
44.3%
. 287562
39.5%
& 35771
 
4.9%
' 31617
 
4.3%
/ 23030
 
3.2%
# 16312
 
2.2%
: 8146
 
1.1%
; 2946
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 16916
91.0%
[ 1679
 
9.0%
Close Punctuation
ValueCountFrequency (%)
) 14019
89.3%
] 1679
 
10.7%
Space Separator
ValueCountFrequency (%)
2563925
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 79112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12030177
67.2%
Common 5884548
32.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 999774
 
8.3%
A 929165
 
7.7%
S 826288
 
6.9%
T 822465
 
6.8%
R 722187
 
6.0%
N 659055
 
5.5%
O 605493
 
5.0%
L 568918
 
4.7%
I 467137
 
3.9%
e 397181
 
3.3%
Other values (40) 5032514
41.8%
Common
ValueCountFrequency (%)
2563925
43.6%
1 469505
 
8.0%
0 445153
 
7.6%
, 321907
 
5.5%
2 305413
 
5.2%
. 287562
 
4.9%
5 262629
 
4.5%
3 228815
 
3.9%
4 191912
 
3.3%
6 166125
 
2.8%
Other values (14) 641602
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17914725
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2563925
 
14.3%
E 999774
 
5.6%
A 929165
 
5.2%
S 826288
 
4.6%
T 822465
 
4.6%
R 722187
 
4.0%
N 659055
 
3.7%
O 605493
 
3.4%
L 568918
 
3.2%
1 469505
 
2.6%
Other values (64) 8747950
48.8%

State
Categorical

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.2 MiB
California
201244 
Pennsylvania
54142 
Texas
38980 
Arizona
 
25118
New York
 
22685
Other values (43)
323245 

Length

Max length20
Median length14
Mean length8.9479467
Min length4

Characters and Unicode

Total characters5954089
Distinct characters46
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArizona
2nd rowArizona
3rd rowArizona
4th rowArizona
5th rowArizona

Common Values

ValueCountFrequency (%)
California 201244
30.2%
Pennsylvania 54142
 
8.1%
Texas 38980
 
5.9%
Arizona 25118
 
3.8%
New York 22685
 
3.4%
Virginia 20571
 
3.1%
Colorado 16198
 
2.4%
North Carolina 14449
 
2.2%
Illinois 13803
 
2.1%
Ohio 13376
 
2.0%
Other values (38) 244848
36.8%

Length

2025-03-29T01:28:17.678631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
california 201244
26.5%
pennsylvania 54142
 
7.1%
new 43204
 
5.7%
texas 38980
 
5.1%
arizona 25118
 
3.3%
york 22685
 
3.0%
virginia 20571
 
2.7%
north 19643
 
2.6%
colorado 16198
 
2.1%
carolina 16128
 
2.1%
Other values (42) 302598
39.8%

Most occurring characters

ValueCountFrequency (%)
a 931250
15.6%
i 768023
12.9%
n 575266
9.7%
o 483055
 
8.1%
r 405966
 
6.8%
l 371690
 
6.2%
s 263744
 
4.4%
C 254342
 
4.3%
e 251851
 
4.2%
f 211761
 
3.6%
Other values (36) 1437141
24.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5098481
85.6%
Uppercase Letter 760511
 
12.8%
Space Separator 95097
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 931250
18.3%
i 768023
15.1%
n 575266
11.3%
o 483055
9.5%
r 405966
8.0%
l 371690
 
7.3%
s 263744
 
5.2%
e 251851
 
4.9%
f 211761
 
4.2%
t 119348
 
2.3%
Other values (14) 716527
14.1%
Uppercase Letter
ValueCountFrequency (%)
C 254342
33.4%
N 69956
 
9.2%
P 54142
 
7.1%
M 51736
 
6.8%
T 43117
 
5.7%
O 41112
 
5.4%
A 40606
 
5.3%
I 36870
 
4.8%
V 23156
 
3.0%
D 22761
 
3.0%
Other values (11) 122713
16.1%
Space Separator
ValueCountFrequency (%)
95097
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5858992
98.4%
Common 95097
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 931250
15.9%
i 768023
13.1%
n 575266
9.8%
o 483055
 
8.2%
r 405966
 
6.9%
l 371690
 
6.3%
s 263744
 
4.5%
C 254342
 
4.3%
e 251851
 
4.3%
f 211761
 
3.6%
Other values (35) 1342044
22.9%
Common
ValueCountFrequency (%)
95097
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5954089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 931250
15.6%
i 768023
12.9%
n 575266
9.7%
o 483055
 
8.1%
r 405966
 
6.8%
l 371690
 
6.2%
s 263744
 
4.4%
C 254342
 
4.3%
e 251851
 
4.2%
f 211761
 
3.6%
Other values (36) 1437141
24.1%

County
Text

Distinct137
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.2 MiB
2025-03-29T01:28:17.825073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length21
Median length15
Mean length8.6726384
Min length3

Characters and Unicode

Total characters5770895
Distinct characters51
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMaricopa
2nd rowMaricopa
3rd rowMaricopa
4th rowMaricopa
5th rowMaricopa
ValueCountFrequency (%)
santa 36370
 
4.0%
los 33838
 
3.8%
angeles 33838
 
3.8%
san 32169
 
3.6%
contra 28835
 
3.2%
costa 28835
 
3.2%
barbara 26608
 
3.0%
maricopa 16717
 
1.9%
riverside 15671
 
1.7%
harris 15397
 
1.7%
Other values (143) 631647
70.2%
2025-03-29T01:28:18.072661image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 735123
 
12.7%
o 451535
 
7.8%
e 434542
 
7.5%
n 417503
 
7.2%
r 395449
 
6.9%
i 300777
 
5.2%
l 287463
 
5.0%
t 286981
 
5.0%
s 280648
 
4.9%
236027
 
4.1%
Other values (41) 1944847
33.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4625504
80.2%
Uppercase Letter 897707
 
15.6%
Space Separator 236027
 
4.1%
Other Punctuation 11657
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 735123
15.9%
o 451535
9.8%
e 434542
9.4%
n 417503
9.0%
r 395449
8.5%
i 300777
 
6.5%
l 287463
 
6.2%
t 286981
 
6.2%
s 280648
 
6.1%
u 150767
 
3.3%
Other values (14) 884716
19.1%
Uppercase Letter
ValueCountFrequency (%)
C 142651
15.9%
S 135807
15.1%
B 84939
9.5%
L 72836
8.1%
A 69257
7.7%
H 59764
 
6.7%
M 50370
 
5.6%
P 50085
 
5.6%
D 46693
 
5.2%
R 31351
 
3.5%
Other values (14) 153954
17.1%
Other Punctuation
ValueCountFrequency (%)
. 7497
64.3%
' 4160
35.7%
Space Separator
ValueCountFrequency (%)
236027
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5523211
95.7%
Common 247684
 
4.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 735123
13.3%
o 451535
 
8.2%
e 434542
 
7.9%
n 417503
 
7.6%
r 395449
 
7.2%
i 300777
 
5.4%
l 287463
 
5.2%
t 286981
 
5.2%
s 280648
 
5.1%
u 150767
 
2.7%
Other values (38) 1782423
32.3%
Common
ValueCountFrequency (%)
236027
95.3%
. 7497
 
3.0%
' 4160
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5770895
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 735123
 
12.7%
o 451535
 
7.8%
e 434542
 
7.5%
n 417503
 
7.2%
r 395449
 
6.9%
i 300777
 
5.2%
l 287463
 
5.0%
t 286981
 
5.0%
s 280648
 
4.9%
236027
 
4.1%
Other values (41) 1944847
33.7%

City
Text

Distinct150
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.2 MiB
2025-03-29T01:28:18.263918image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length48
Median length24
Mean length9.6480507
Min length4

Characters and Unicode

Total characters6419948
Distinct characters53
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPhoenix
2nd rowPhoenix
3rd rowPhoenix
4th rowPhoenix
5th rowPhoenix
ValueCountFrequency (%)
city 75619
 
6.8%
not 54312
 
4.9%
in 54312
 
4.9%
a 54312
 
4.9%
san 25302
 
2.3%
new 24120
 
2.2%
york 21290
 
1.9%
angeles 19903
 
1.8%
los 19903
 
1.8%
park 19042
 
1.7%
Other values (177) 736518
66.7%
2025-03-29T01:28:18.520799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 540670
 
8.4%
o 515660
 
8.0%
e 506944
 
7.9%
n 474354
 
7.4%
439219
 
6.8%
t 430941
 
6.7%
i 415651
 
6.5%
l 336542
 
5.2%
s 297793
 
4.6%
r 294592
 
4.6%
Other values (43) 2167582
33.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4997741
77.8%
Uppercase Letter 950327
 
14.8%
Space Separator 439219
 
6.8%
Other Punctuation 8347
 
0.1%
Dash Punctuation 8318
 
0.1%
Close Punctuation 7998
 
0.1%
Open Punctuation 7998
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 540670
10.8%
o 515660
10.3%
e 506944
10.1%
n 474354
9.5%
t 430941
8.6%
i 415651
8.3%
l 336542
 
6.7%
s 297793
 
6.0%
r 294592
 
5.9%
c 169905
 
3.4%
Other values (15) 1014689
20.3%
Uppercase Letter
ValueCountFrequency (%)
C 105983
11.2%
N 100360
10.6%
P 88339
 
9.3%
L 82555
 
8.7%
B 64777
 
6.8%
S 63597
 
6.7%
R 56436
 
5.9%
A 53160
 
5.6%
E 46729
 
4.9%
D 41994
 
4.4%
Other values (13) 246397
25.9%
Space Separator
ValueCountFrequency (%)
439219
100.0%
Other Punctuation
ValueCountFrequency (%)
. 8347
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8318
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7998
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7998
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5948068
92.6%
Common 471880
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 540670
 
9.1%
o 515660
 
8.7%
e 506944
 
8.5%
n 474354
 
8.0%
t 430941
 
7.2%
i 415651
 
7.0%
l 336542
 
5.7%
s 297793
 
5.0%
r 294592
 
5.0%
c 169905
 
2.9%
Other values (38) 1965016
33.0%
Common
ValueCountFrequency (%)
439219
93.1%
. 8347
 
1.8%
- 8318
 
1.8%
) 7998
 
1.7%
( 7998
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6419948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 540670
 
8.4%
o 515660
 
8.0%
e 506944
 
7.9%
n 474354
 
7.4%
439219
 
6.8%
t 430941
 
6.7%
i 415651
 
6.5%
l 336542
 
5.2%
s 297793
 
4.6%
r 294592
 
4.6%
Other values (43) 2167582
33.8%

O3 Mean
Real number (ℝ)

HIGH CORRELATION 

Distinct5780
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.028604842
Minimum-0.000706
Maximum0.107353
Zeros251
Zeros (%)< 0.1%
Negative2
Negative (%)< 0.1%
Memory size10.2 MiB
2025-03-29T01:28:18.631973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-0.000706
5-th percentile0.009176
Q10.019824
median0.028353
Q30.036882
95-th percentile0.048941
Maximum0.107353
Range0.108059
Interquartile range (IQR)0.017058

Descriptive statistics

Standard deviation0.012150823
Coefficient of variation (CV)0.42478202
Kurtosis-0.12560318
Mean0.028604842
Median Absolute Deviation (MAD)0.008529
Skewness0.22038664
Sum19034.062
Variance0.00014764249
MonotonicityNot monotonic
2025-03-29T01:28:18.716121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.029 1275
 
0.2%
0.03 1266
 
0.2%
0.024 1251
 
0.2%
0.031 1249
 
0.2%
0.028 1247
 
0.2%
0.029118 1239
 
0.2%
0.030059 1238
 
0.2%
0.032 1228
 
0.2%
0.026588 1227
 
0.2%
0.028529 1224
 
0.2%
Other values (5770) 652970
98.1%
ValueCountFrequency (%)
-0.000706 1
 
< 0.1%
-0.000529 1
 
< 0.1%
0 251
< 0.1%
5.9 × 10-54
 
< 0.1%
8.3 × 10-51
 
< 0.1%
0.000111 1
 
< 0.1%
0.000118 14
 
< 0.1%
0.000143 1
 
< 0.1%
0.000167 2
 
< 0.1%
0.000176 30
 
< 0.1%
ValueCountFrequency (%)
0.107353 1
< 0.1%
0.1016 1
< 0.1%
0.101235 1
< 0.1%
0.096765 1
< 0.1%
0.095706 1
< 0.1%
0.094647 1
< 0.1%
0.0944 1
< 0.1%
0.094294 1
< 0.1%
0.093588 1
< 0.1%
0.092588 1
< 0.1%

O3 1st Max Value
Real number (ℝ)

HIGH CORRELATION 

Distinct136
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.038980463
Minimum0
Maximum0.14
Zeros251
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size10.2 MiB
2025-03-29T01:28:18.797519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.016
Q10.029
median0.038
Q30.048
95-th percentile0.065
Maximum0.14
Range0.14
Interquartile range (IQR)0.019

Descriptive statistics

Standard deviation0.014911814
Coefficient of variation (CV)0.38254584
Kurtosis0.66877922
Mean0.038980463
Median Absolute Deviation (MAD)0.01
Skewness0.46871935
Sum25938.146
Variance0.0002223622
MonotonicityNot monotonic
2025-03-29T01:28:18.880657image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.037 19143
 
2.9%
0.038 18940
 
2.8%
0.035 18928
 
2.8%
0.036 18926
 
2.8%
0.039 18836
 
2.8%
0.034 18817
 
2.8%
0.033 18749
 
2.8%
0.04 18178
 
2.7%
0.041 18139
 
2.7%
0.032 18136
 
2.7%
Other values (126) 478622
71.9%
ValueCountFrequency (%)
0 251
 
< 0.1%
0.001 415
 
0.1%
0.002 612
 
0.1%
0.003 746
0.1%
0.004 914
0.1%
0.005 1029
0.2%
0.006 1255
0.2%
0.007 1355
0.2%
0.007 60
 
< 0.1%
0.008 1743
0.3%
ValueCountFrequency (%)
0.14 1
 
< 0.1%
0.132 2
< 0.1%
0.131 1
 
< 0.1%
0.13 2
< 0.1%
0.129 1
 
< 0.1%
0.128 4
< 0.1%
0.127 2
< 0.1%
0.126 4
< 0.1%
0.125 2
< 0.1%
0.124 4
< 0.1%

O3 1st Max Hour
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.766409
Minimum7
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 MiB
2025-03-29T01:28:18.956009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7
Q19
median10
Q311
95-th percentile20
Maximum23
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.2973146
Coefficient of variation (CV)0.30625947
Kurtosis4.8839852
Mean10.766409
Median Absolute Deviation (MAD)1
Skewness2.1980663
Sum7164119
Variance10.872283
MonotonicityNot monotonic
2025-03-29T01:28:19.021711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
10 186858
28.1%
9 135540
20.4%
11 116654
17.5%
8 54016
 
8.1%
7 43963
 
6.6%
12 41042
 
6.2%
13 17193
 
2.6%
23 12662
 
1.9%
14 8913
 
1.3%
22 8222
 
1.2%
Other values (7) 40351
 
6.1%
ValueCountFrequency (%)
7 43963
 
6.6%
8 54016
 
8.1%
9 135540
20.4%
10 186858
28.1%
11 116654
17.5%
12 41042
 
6.2%
13 17193
 
2.6%
14 8913
 
1.3%
15 5756
 
0.9%
16 4447
 
0.7%
ValueCountFrequency (%)
23 12662
1.9%
22 8222
1.2%
21 7842
1.2%
20 7228
1.1%
19 5933
0.9%
18 4785
 
0.7%
17 4360
 
0.7%
16 4447
 
0.7%
15 5756
0.9%
14 8913
1.3%

O3 AQI
Real number (ℝ)

HIGH CORRELATION 

Distinct130
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.137872
Minimum0
Maximum237
Zeros251
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size10.2 MiB
2025-03-29T01:28:19.105083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q127
median35
Q344
95-th percentile84
Maximum237
Range237
Interquartile range (IQR)17

Descriptive statistics

Standard deviation22.253413
Coefficient of variation (CV)0.56859027
Kurtosis9.4401494
Mean39.137872
Median Absolute Deviation (MAD)9
Skewness2.484009
Sum26042888
Variance495.2144
MonotonicityNot monotonic
2025-03-29T01:28:19.190835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 37562
 
5.6%
44 27245
 
4.1%
34 19139
 
2.9%
35 18938
 
2.8%
32 18925
 
2.8%
33 18921
 
2.8%
36 18835
 
2.8%
37 18177
 
2.7%
38 18139
 
2.7%
30 18135
 
2.7%
Other values (120) 451398
67.8%
ValueCountFrequency (%)
0 251
 
< 0.1%
1 415
 
0.1%
2 610
 
0.1%
3 746
 
0.1%
4 913
 
0.1%
5 1028
 
0.2%
6 2670
0.4%
7 1742
0.3%
8 2007
0.3%
9 2186
0.3%
ValueCountFrequency (%)
237 1
 
< 0.1%
228 2
< 0.1%
227 1
 
< 0.1%
226 2
< 0.1%
225 1
 
< 0.1%
224 4
< 0.1%
223 2
< 0.1%
222 4
< 0.1%
221 2
< 0.1%
220 4
< 0.1%

CO Mean
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3102
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.32945852
Minimum-0.4375
Maximum7.508333
Zeros21340
Zeros (%)3.2%
Negative1939
Negative (%)0.3%
Memory size10.2 MiB
2025-03-29T01:28:19.275988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-0.4375
5-th percentile0.033333
Q10.175
median0.258333
Q30.408696
95-th percentile0.833333
Maximum7.508333
Range7.945833
Interquartile range (IQR)0.233696

Descriptive statistics

Standard deviation0.27572475
Coefficient of variation (CV)0.83690278
Kurtosis17.60964
Mean0.32945852
Median Absolute Deviation (MAD)0.108334
Skewness2.9021215
Sum219226.31
Variance0.07602414
MonotonicityNot monotonic
2025-03-29T01:28:19.357968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 52892
 
7.9%
0.1 32083
 
4.8%
0 21340
 
3.2%
0.3 17340
 
2.6%
0.4 7484
 
1.1%
0.233333 7329
 
1.1%
0.216667 7076
 
1.1%
0.2125 6608
 
1.0%
0.225 6575
 
1.0%
0.208333 6478
 
1.0%
Other values (3092) 500209
75.2%
ValueCountFrequency (%)
-0.4375 1
 
< 0.1%
-0.4 30
< 0.1%
-0.395833 1
 
< 0.1%
-0.391667 3
 
< 0.1%
-0.3875 2
 
< 0.1%
-0.375 2
 
< 0.1%
-0.370833 2
 
< 0.1%
-0.366667 3
 
< 0.1%
-0.3625 5
 
< 0.1%
-0.358824 1
 
< 0.1%
ValueCountFrequency (%)
7.508333 1
< 0.1%
6.975 1
< 0.1%
5.779167 1
< 0.1%
5.633333 1
< 0.1%
5.2625 1
< 0.1%
5.195833 1
< 0.1%
5.0375 1
< 0.1%
5.029167 1
< 0.1%
4.995833 1
< 0.1%
4.966667 1
< 0.1%

CO 1st Max Value
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct105
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46560382
Minimum-0.4
Maximum15.5
Zeros22094
Zeros (%)3.3%
Negative949
Negative (%)0.1%
Memory size10.2 MiB
2025-03-29T01:28:19.447936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-0.4
5-th percentile0.1
Q10.2
median0.3
Q30.6
95-th percentile1.2
Maximum15.5
Range15.9
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.43454208
Coefficient of variation (CV)0.93328719
Kurtosis33.939263
Mean0.46560382
Median Absolute Deviation (MAD)0.1
Skewness3.784804
Sum309819.3
Variance0.18882682
MonotonicityNot monotonic
2025-03-29T01:28:19.531577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 138521
20.8%
0.3 123735
18.6%
0.4 86774
13.0%
0.5 60814
9.1%
0.1 55049
 
8.3%
0.6 43480
 
6.5%
0.7 31112
 
4.7%
0.8 23012
 
3.5%
0 22094
 
3.3%
0.9 16988
 
2.6%
Other values (95) 63835
9.6%
ValueCountFrequency (%)
-0.4 31
 
< 0.1%
-0.3 125
 
< 0.1%
-0.2 247
 
< 0.1%
-0.1 546
 
0.1%
0 22094
 
3.3%
0.1 55049
 
8.3%
0.2 138521
20.8%
0.3 123735
18.6%
0.4 86774
13.0%
0.5 60814
9.1%
ValueCountFrequency (%)
15.5 1
< 0.1%
14.4 1
< 0.1%
13.8 1
< 0.1%
13 1
< 0.1%
11.7 1
< 0.1%
11.6 2
< 0.1%
11 2
< 0.1%
10.5 1
< 0.1%
10.3 1
< 0.1%
10.2 1
< 0.1%

CO 1st Max Hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9225535
Minimum0
Maximum23
Zeros310027
Zeros (%)46.6%
Negative0
Negative (%)0.0%
Memory size10.2 MiB
2025-03-29T01:28:19.606085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.7195366
Coefficient of variation (CV)1.3034136
Kurtosis-0.048672501
Mean5.9225535
Median Absolute Deviation (MAD)1
Skewness1.140216
Sum3940950
Variance59.591245
MonotonicityNot monotonic
2025-03-29T01:28:19.671639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 310027
46.6%
23 41143
 
6.2%
7 34264
 
5.1%
8 31420
 
4.7%
6 26000
 
3.9%
1 24106
 
3.6%
9 23093
 
3.5%
22 22377
 
3.4%
2 18289
 
2.7%
10 17710
 
2.7%
Other values (14) 116985
 
17.6%
ValueCountFrequency (%)
0 310027
46.6%
1 24106
 
3.6%
2 18289
 
2.7%
3 13634
 
2.0%
4 10846
 
1.6%
5 15872
 
2.4%
6 26000
 
3.9%
7 34264
 
5.1%
8 31420
 
4.7%
9 23093
 
3.5%
ValueCountFrequency (%)
23 41143
6.2%
22 22377
3.4%
21 13789
 
2.1%
20 9589
 
1.4%
19 6814
 
1.0%
18 5049
 
0.8%
17 4137
 
0.6%
16 3690
 
0.6%
15 3555
 
0.5%
14 3960
 
0.6%

CO AQI
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2473994
Minimum0
Maximum201
Zeros23043
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size10.2 MiB
2025-03-29T01:28:19.747883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q37
95-th percentile14
Maximum201
Range201
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.0104671
Coefficient of variation (CV)0.95484768
Kurtosis33.729129
Mean5.2473994
Median Absolute Deviation (MAD)2
Skewness3.6248839
Sum3491693
Variance25.104781
MonotonicityNot monotonic
2025-03-29T01:28:19.834022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 138520
20.8%
3 123734
18.6%
5 86769
13.0%
6 60816
9.1%
1 55049
 
8.3%
7 43481
 
6.5%
8 31111
 
4.7%
0 23043
 
3.5%
9 23012
 
3.5%
10 16988
 
2.6%
Other values (91) 62891
9.5%
ValueCountFrequency (%)
0 23043
 
3.5%
1 55049
 
8.3%
2 138520
20.8%
3 123734
18.6%
5 86769
13.0%
6 60816
9.1%
7 43481
 
6.5%
8 31111
 
4.7%
9 23012
 
3.5%
10 16988
 
2.6%
ValueCountFrequency (%)
201 1
< 0.1%
183 1
< 0.1%
173 1
< 0.1%
159 1
< 0.1%
138 1
< 0.1%
136 2
< 0.1%
126 2
< 0.1%
118 1
< 0.1%
115 1
< 0.1%
113 1
< 0.1%

SO2 Mean
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11350
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4287587
Minimum-2.508333
Maximum321.625
Zeros46666
Zeros (%)7.0%
Negative17414
Negative (%)2.6%
Memory size10.2 MiB
2025-03-29T01:28:19.921842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2.508333
5-th percentile0
Q10.173913
median0.604167
Q31.604545
95-th percentile5.833333
Maximum321.625
Range324.13333
Interquartile range (IQR)1.430632

Descriptive statistics

Standard deviation2.4100712
Coefficient of variation (CV)1.6868288
Kurtosis496.64681
Mean1.4287587
Median Absolute Deviation (MAD)0.520834
Skewness7.5389529
Sum950716.01
Variance5.8084433
MonotonicityNot monotonic
2025-03-29T01:28:20.002434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46666
 
7.0%
1 9516
 
1.4%
0.043478 5993
 
0.9%
2 3900
 
0.6%
0.041667 3754
 
0.6%
0.1 3317
 
0.5%
0.083333 3155
 
0.5%
0.086957 3077
 
0.5%
0.125 2970
 
0.4%
0.5 2891
 
0.4%
Other values (11340) 580175
87.2%
ValueCountFrequency (%)
-2.508333 1
< 0.1%
-2.270833 1
< 0.1%
-2 2
< 0.1%
-1.991667 1
< 0.1%
-1.9 1
< 0.1%
-1.847826 1
< 0.1%
-1.8375 1
< 0.1%
-1.8125 1
< 0.1%
-1.7875 1
< 0.1%
-1.766667 2
< 0.1%
ValueCountFrequency (%)
321.625 1
< 0.1%
81.25 1
< 0.1%
58.565217 1
< 0.1%
56.083333 1
< 0.1%
53.5 1
< 0.1%
51.583333 1
< 0.1%
51.333333 1
< 0.1%
49.708333 1
< 0.1%
49.25 1
< 0.1%
47.833333 1
< 0.1%

SO2 1st Max Value
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct815
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9310574
Minimum-2.3
Maximum351
Zeros52679
Zeros (%)7.9%
Negative4767
Negative (%)0.7%
Memory size10.2 MiB
2025-03-29T01:28:20.086867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2.3
5-th percentile0
Q10.6
median1.4
Q34
95-th percentile16
Maximum351
Range353.3
Interquartile range (IQR)3.4

Descriptive statistics

Standard deviation7.7007985
Coefficient of variation (CV)1.9589637
Kurtosis89.581841
Mean3.9310574
Median Absolute Deviation (MAD)1.1
Skewness6.5580224
Sum2615780.6
Variance59.302298
MonotonicityNot monotonic
2025-03-29T01:28:20.168044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 62771
 
9.4%
0 52679
 
7.9%
2 44348
 
6.7%
3 30853
 
4.6%
0.2 23188
 
3.5%
0.3 23049
 
3.5%
4 22766
 
3.4%
0.4 22229
 
3.3%
0.5 21369
 
3.2%
0.6 19695
 
3.0%
Other values (805) 342467
51.5%
ValueCountFrequency (%)
-2.3 1
 
< 0.1%
-2 3
 
< 0.1%
-1.7 2
 
< 0.1%
-1.6 7
 
< 0.1%
-1.5 7
 
< 0.1%
-1.4 6
 
< 0.1%
-1.3 4
 
< 0.1%
-1.2 5
 
< 0.1%
-1.1 16
 
< 0.1%
-1 57
< 0.1%
ValueCountFrequency (%)
351 1
< 0.1%
327 1
< 0.1%
292 1
< 0.1%
257 1
< 0.1%
249 1
< 0.1%
248 1
< 0.1%
247 1
< 0.1%
245 1
< 0.1%
240 2
< 0.1%
237 1
< 0.1%

SO2 1st Max Hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8672796
Minimum0
Maximum23
Zeros114884
Zeros (%)17.3%
Negative0
Negative (%)0.0%
Memory size10.2 MiB
2025-03-29T01:28:20.242673image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median8
Q313
95-th percentile21
Maximum23
Range23
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.7767792
Coefficient of variation (CV)0.76424558
Kurtosis-0.80752355
Mean8.8672796
Median Absolute Deviation (MAD)5
Skewness0.39627433
Sum5900412
Variance45.924737
MonotonicityNot monotonic
2025-03-29T01:28:20.310160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 114884
17.3%
7 46843
 
7.0%
8 46807
 
7.0%
9 42048
 
6.3%
10 36014
 
5.4%
6 32942
 
5.0%
11 30424
 
4.6%
5 27805
 
4.2%
12 25538
 
3.8%
2 22756
 
3.4%
Other values (14) 239353
36.0%
ValueCountFrequency (%)
0 114884
17.3%
1 21449
 
3.2%
2 22756
 
3.4%
3 16000
 
2.4%
4 16957
 
2.5%
5 27805
 
4.2%
6 32942
 
5.0%
7 46843
7.0%
8 46807
7.0%
9 42048
 
6.3%
ValueCountFrequency (%)
23 16773
2.5%
22 16327
2.5%
21 16257
2.4%
20 17764
2.7%
19 15154
2.3%
18 14901
2.2%
17 15029
2.3%
16 15926
2.4%
15 16838
2.5%
14 18510
2.8%

SO2 AQI
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct142
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1547428
Minimum0
Maximum200
Zeros234459
Zeros (%)35.2%
Negative0
Negative (%)0.0%
Memory size10.2 MiB
2025-03-29T01:28:20.385826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile23
Maximum200
Range200
Interquartile range (IQR)6

Descriptive statistics

Standard deviation10.371465
Coefficient of variation (CV)2.0120238
Kurtosis30.280685
Mean5.1547428
Median Absolute Deviation (MAD)1
Skewness4.5456955
Sum3430038
Variance107.56729
MonotonicityNot monotonic
2025-03-29T01:28:20.469266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 234459
35.2%
1 135867
20.4%
3 74552
 
11.2%
4 45485
 
6.8%
6 30900
 
4.6%
7 22849
 
3.4%
9 17385
 
2.6%
10 13760
 
2.1%
11 11545
 
1.7%
13 9259
 
1.4%
Other values (132) 69353
 
10.4%
ValueCountFrequency (%)
0 234459
35.2%
1 135867
20.4%
3 74552
 
11.2%
4 45485
 
6.8%
6 30900
 
4.6%
7 22849
 
3.4%
9 17385
 
2.6%
10 13760
 
2.1%
11 11545
 
1.7%
13 9259
 
1.4%
ValueCountFrequency (%)
200 2
< 0.1%
195 1
< 0.1%
180 1
< 0.1%
177 2
< 0.1%
176 2
< 0.1%
173 2
< 0.1%
172 1
< 0.1%
169 1
< 0.1%
168 2
< 0.1%
167 1
< 0.1%

NO2 Mean
Real number (ℝ)

HIGH CORRELATION 

Distinct41004
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.510561
Minimum-4.629167
Maximum140.65
Zeros3273
Zeros (%)0.5%
Negative927
Negative (%)0.1%
Memory size10.2 MiB
2025-03-29T01:28:20.553673image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-4.629167
5-th percentile1.26087
Q14.86087
median9.304348
Q315.958333
95-th percentile29.082862
Maximum140.65
Range145.27917
Interquartile range (IQR)11.097463

Descriptive statistics

Standard deviation8.957527
Coefficient of variation (CV)0.77820076
Kurtosis3.085675
Mean11.510561
Median Absolute Deviation (MAD)5.173913
Skewness1.4116404
Sum7659288.2
Variance80.23729
MonotonicityNot monotonic
2025-03-29T01:28:20.634095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3273
 
0.5%
7 860
 
0.1%
6 851
 
0.1%
1 844
 
0.1%
8 840
 
0.1%
4 806
 
0.1%
9 790
 
0.1%
5 786
 
0.1%
11 782
 
0.1%
3 767
 
0.1%
Other values (40994) 654815
98.4%
ValueCountFrequency (%)
-4.629167 1
< 0.1%
-4.529167 1
< 0.1%
-4.463636 1
< 0.1%
-4.4625 1
< 0.1%
-4.454167 1
< 0.1%
-4.45 1
< 0.1%
-4.35 1
< 0.1%
-4.3 1
< 0.1%
-4.2 1
< 0.1%
-4.19 1
< 0.1%
ValueCountFrequency (%)
140.65 1
< 0.1%
139.541667 1
< 0.1%
135.333333 1
< 0.1%
135.1875 1
< 0.1%
123.333333 1
< 0.1%
113.083333 1
< 0.1%
110.136364 1
< 0.1%
107.545455 1
< 0.1%
105.5 1
< 0.1%
98.75 1
< 0.1%

NO2 1st Max Value
Real number (ℝ)

HIGH CORRELATION 

Distinct1018
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.253519
Minimum-4.4
Maximum371.7
Zeros3554
Zeros (%)0.5%
Negative165
Negative (%)< 0.1%
Memory size10.2 MiB
2025-03-29T01:28:20.719533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-4.4
5-th percentile3
Q111
median21
Q333
95-th percentile50
Maximum371.7
Range376.1
Interquartile range (IQR)22

Descriptive statistics

Standard deviation15.264335
Coefficient of variation (CV)0.65643118
Kurtosis4.5953259
Mean23.253519
Median Absolute Deviation (MAD)11
Skewness1.0448285
Sum15473217
Variance232.99992
MonotonicityNot monotonic
2025-03-29T01:28:20.801499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 9020
 
1.4%
22 8900
 
1.3%
16 8851
 
1.3%
21 8827
 
1.3%
18 8778
 
1.3%
19 8722
 
1.3%
23 8713
 
1.3%
24 8704
 
1.3%
26 8703
 
1.3%
17 8582
 
1.3%
Other values (1008) 577614
86.8%
ValueCountFrequency (%)
-4.4 1
< 0.1%
-4.2 2
< 0.1%
-4 1
< 0.1%
-3.9 2
< 0.1%
-3.8 1
< 0.1%
-3.6 2
< 0.1%
-3.5 1
< 0.1%
-3.2 1
< 0.1%
-3 1
< 0.1%
-2.8 1
< 0.1%
ValueCountFrequency (%)
371.7 1
< 0.1%
369.9 1
< 0.1%
367.6 1
< 0.1%
361.6 1
< 0.1%
296.4 1
< 0.1%
269.2 1
< 0.1%
267 1
< 0.1%
262 1
< 0.1%
256 1
< 0.1%
251 1
< 0.1%

NO2 1st Max Hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.588897
Minimum0
Maximum23
Zeros61611
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size10.2 MiB
2025-03-29T01:28:20.890650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median9
Q320
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)15

Descriptive statistics

Standard deviation7.8883008
Coefficient of variation (CV)0.68067748
Kurtosis-1.5390622
Mean11.588897
Median Absolute Deviation (MAD)8
Skewness0.068587382
Sum7711414
Variance62.22529
MonotonicityNot monotonic
2025-03-29T01:28:20.959093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
6 70131
 
10.5%
0 61611
 
9.3%
7 57836
 
8.7%
20 48935
 
7.4%
21 45871
 
6.9%
19 44351
 
6.7%
5 43556
 
6.5%
23 40986
 
6.2%
22 38605
 
5.8%
18 34636
 
5.2%
Other values (14) 178896
26.9%
ValueCountFrequency (%)
0 61611
9.3%
1 19762
 
3.0%
2 15663
 
2.4%
3 12376
 
1.9%
4 16995
 
2.6%
5 43556
6.5%
6 70131
10.5%
7 57836
8.7%
8 32229
4.8%
9 18535
 
2.8%
ValueCountFrequency (%)
23 40986
6.2%
22 38605
5.8%
21 45871
6.9%
20 48935
7.4%
19 44351
6.7%
18 34636
5.2%
17 18955
 
2.8%
16 8236
 
1.2%
15 5112
 
0.8%
14 4265
 
0.6%

NO2 AQI
Real number (ℝ)

HIGH CORRELATION 

Distinct134
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.766209
Minimum0
Maximum153
Zeros6382
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size10.2 MiB
2025-03-29T01:28:21.036362image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q110
median20
Q331
95-th percentile47
Maximum153
Range153
Interquartile range (IQR)21

Descriptive statistics

Standard deviation14.44778
Coefficient of variation (CV)0.66377107
Kurtosis1.5159084
Mean21.766209
Median Absolute Deviation (MAD)10
Skewness0.91227346
Sum14483540
Variance208.73834
MonotonicityNot monotonic
2025-03-29T01:28:21.123060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 34644
 
5.2%
25 28182
 
4.2%
11 17373
 
2.6%
9 17211
 
2.6%
7 17035
 
2.6%
10 16961
 
2.5%
12 16765
 
2.5%
13 16627
 
2.5%
6 16571
 
2.5%
14 16505
 
2.5%
Other values (124) 467540
70.3%
ValueCountFrequency (%)
0 6382
 
1.0%
1 11137
 
1.7%
2 12897
 
1.9%
3 13506
 
2.0%
4 14864
2.2%
5 16094
2.4%
6 16571
2.5%
7 17035
2.6%
8 34644
5.2%
9 17211
2.6%
ValueCountFrequency (%)
153 1
< 0.1%
152 2
< 0.1%
151 1
< 0.1%
138 1
< 0.1%
133 1
< 0.1%
132 1
< 0.1%
131 1
< 0.1%
130 1
< 0.1%
129 1
< 0.1%
128 2
< 0.1%

Interactions

2025-03-29T01:28:11.484759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:31.512699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:34.529797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:37.240310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:40.680103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:43.215478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:45.908193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:48.380682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:50.928651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:53.577356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:55.930631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:58.397089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:01.195885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:03.684561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:06.345177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:08.913537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:11.644325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:31.687034image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:34.707534image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:37.402815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:40.838128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:43.371355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:46.065313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:48.540124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:51.086440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:53.724861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:56.077975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:58.560489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:01.347139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:03.845102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:06.503345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:09.075056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:11.816955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:31.876697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:34.885986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:37.593802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:40.992458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:43.533048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:46.224036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:48.702112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:51.245699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:53.877860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:56.233111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:58.714325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:01.508767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:04.016683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:06.674571image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:09.232276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:12.268696image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:32.042050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:35.071495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:37.759512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:41.152880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:43.701105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:46.381820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:48.856547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:51.409141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:54.027445image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:56.387176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:58.873650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:01.675278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:04.190216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:06.846576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:09.392339image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:12.431439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:32.201788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:35.240302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:37.925970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:41.303576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:43.864840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:46.527813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:49.014365image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:51.556421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:54.163497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:56.528714image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:59.024231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:01.823898image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:04.342497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:06.993941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:09.543799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:12.596377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:32.360166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:35.401292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:38.818928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:41.467353image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:44.013141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:46.675869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:49.171821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:51.707946image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:54.312797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:56.675874image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:59.180700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:01.982842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:04.497935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:07.153307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:09.704942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:12.757278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:32.538926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:35.578313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:39.039694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:41.631084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:44.169476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:46.837662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:49.341628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:51.871304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:54.464891image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:56.833848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:59.333672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:02.142817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:04.672450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:07.320610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:09.859104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:12.916978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:32.695820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:35.748332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:39.198655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:41.785127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:44.327319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:46.981453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:49.500732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:52.184518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:54.601676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:56.974412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:59.487563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:02.288431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:04.830913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:07.468555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:10.012658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:13.081895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:32.866322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:35.922258image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:39.365336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:41.943546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:44.479054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:47.130229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:49.664691image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:52.346869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:54.746310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:57.124722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:59.649089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:02.438845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:05.051579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:07.627287image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:10.173177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:13.243073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:33.078021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:36.088644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:39.525738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:42.099675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:44.628573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:47.277828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:49.822026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:52.495210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:54.887565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:57.270020image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:59.820356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:02.595898image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:05.212182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:07.781352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:10.341929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:13.400485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:33.287165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:36.262482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:39.696517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:42.264163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:44.957077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:47.436291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:49.981375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:52.653451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:55.041025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:57.424157image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:59.974166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:02.751896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:05.387755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:07.955215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:10.496455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:13.558646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:33.520390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:36.424414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:39.857663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:42.419200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:45.103659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:47.582983image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:50.132087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:52.801967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:55.177898image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:57.565908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:00.135023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:02.907831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:05.539475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:08.107863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:10.649851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:13.724577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:33.701268image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:36.585566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:40.029303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:42.586102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:45.259015image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:47.743142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:50.292543image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:52.954475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:55.324011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:57.716209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:00.300069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:03.063357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:05.703051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:08.269744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:10.814124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:13.884924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:33.879649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:36.746322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:40.194789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:42.749712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:45.444433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:47.892612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:50.448836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:53.106447image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:55.468028image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:57.870103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:00.459412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:03.216980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:05.867483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:08.430591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:10.972873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:14.045363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:34.060050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:36.913288image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:40.355975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:42.910546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:45.601079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:48.053595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:50.600909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:53.273810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:55.617747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:58.026263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:00.619784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:03.377069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:06.027539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:08.591309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:11.129329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:14.196129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:34.291938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:37.073400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:40.507716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:43.062477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:45.750661image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:48.202952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:50.750697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:53.424612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:55.777582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:27:58.174627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:01.039987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:03.527998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:06.183197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:08.744925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-03-29T01:28:11.279782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2025-03-29T01:28:21.213752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
CO 1st Max HourCO 1st Max ValueCO AQICO MeanNO2 1st Max HourNO2 1st Max ValueNO2 AQINO2 MeanO3 1st Max HourO3 1st Max ValueO3 AQIO3 MeanSO2 1st Max HourSO2 1st Max ValueSO2 AQISO2 MeanState
CO 1st Max Hour1.0000.3700.3700.2420.2190.3810.3810.356-0.040-0.060-0.060-0.2050.1250.1520.1470.1340.089
CO 1st Max Value0.3701.0001.0000.9450.0610.6390.6400.666-0.025-0.113-0.112-0.2870.0730.3560.3500.3450.072
CO AQI0.3701.0001.0000.9450.0610.6390.6400.666-0.025-0.113-0.113-0.2870.0730.3560.3500.3450.045
CO Mean0.2420.9450.9451.0000.0340.5380.5390.591-0.009-0.112-0.112-0.2470.0530.3320.3280.3310.121
NO2 1st Max Hour0.2190.0610.0610.0341.0000.1410.1410.110-0.081-0.076-0.076-0.2300.1620.0260.0220.0280.090
NO2 1st Max Value0.3810.6390.6390.5380.1411.0001.0000.934-0.0070.0070.007-0.2410.1490.4490.4280.4090.109
NO2 AQI0.3810.6400.6400.5390.1411.0001.0000.934-0.0070.0070.007-0.2410.1470.4540.4350.4130.142
NO2 Mean0.3560.6660.6660.5910.1100.9340.9341.0000.006-0.097-0.097-0.3300.1500.4770.4580.4440.135
O3 1st Max Hour-0.040-0.025-0.025-0.009-0.081-0.007-0.0070.0061.0000.1370.1380.192-0.0130.0290.0300.0200.104
O3 1st Max Value-0.060-0.113-0.113-0.112-0.0760.0070.007-0.0970.1371.0001.0000.884-0.0080.0060.002-0.0050.095
O3 AQI-0.060-0.112-0.113-0.112-0.0760.0070.007-0.0970.1381.0001.0000.884-0.0080.0060.002-0.0060.084
O3 Mean-0.205-0.287-0.287-0.247-0.230-0.241-0.241-0.3300.1920.8840.8841.000-0.074-0.095-0.094-0.0920.085
SO2 1st Max Hour0.1250.0730.0730.0530.1620.1490.1470.150-0.013-0.008-0.008-0.0741.0000.2850.2140.2110.097
SO2 1st Max Value0.1520.3560.3560.3320.0260.4490.4540.4770.0290.0060.006-0.0950.2851.0000.9740.9150.053
SO2 AQI0.1470.3500.3500.3280.0220.4280.4350.4580.0300.0020.002-0.0940.2140.9741.0000.8730.103
SO2 Mean0.1340.3450.3450.3310.0280.4090.4130.4440.020-0.005-0.006-0.0920.2110.9150.8731.0000.023
State0.0890.0720.0450.1210.0900.1090.1420.1350.1040.0950.0840.0850.0970.0530.1030.0231.000

Missing values

2025-03-29T01:28:14.466024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-29T01:28:15.389953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DateAddressStateCountyCityO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQICO MeanCO 1st Max ValueCO 1st Max HourCO AQISO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQINO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQI
02000-01-011645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix0.0197650.04010370.8789472.22325.03.0000009.02113.019.04166749.01946
12000-01-021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix0.0158820.03210301.0666672.3026.01.9583333.0224.022.95833336.01934
22000-01-031645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix0.0093530.0169151.7625002.5828.05.25000011.01916.038.12500051.0848
32000-01-041645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix0.0158820.0339311.8291673.02334.07.08333316.0823.040.26087074.0872
42000-01-051645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix0.0073530.0129112.7000003.7242.08.70833315.0721.048.45000061.02258
52000-01-061645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix0.0140000.02510232.3083333.6941.06.76190517.0724.039.95000073.0871
62000-01-071645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix0.0132350.02410221.8291673.52340.08.66666721.0730.029.62500043.0941
72000-01-081645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix0.0111760.02010192.7875005.1257.08.25000018.0026.029.66666741.0039
82000-01-091645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix0.0125880.02210201.6750002.8232.06.50000013.01919.025.08333337.02035
92000-01-101645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix0.0100000.0159142.1791673.72342.09.95833321.02030.037.66666770.02068
DateAddressStateCountyCityO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQICO MeanCO 1st Max ValueCO 1st Max HourCO AQISO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQINO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQI
6654042023-06-21NCore - North Cheyenne Soccer ComplexWyomingLaramieCheyenne0.0415290.04712440.00.000.00.0416670.370.02.4583335.605
6654052023-06-22NCore - North Cheyenne Soccer ComplexWyomingLaramieCheyenne0.0406470.0518470.00.000.00.0500000.150.02.3208334.054
6654062023-06-23NCore - North Cheyenne Soccer ComplexWyomingLaramieCheyenne0.0470000.05222480.00.000.00.0826090.3200.02.3434785.475
6654072023-06-24NCore - North Cheyenne Soccer ComplexWyomingLaramieCheyenne0.0402940.0527480.00.000.00.0833330.4210.01.0833333.2213
6654082023-06-25NCore - North Cheyenne Soccer ComplexWyomingLaramieCheyenne0.0444120.05110470.00.000.00.0916670.390.02.0333334.164
6654092023-06-26NCore - North Cheyenne Soccer ComplexWyomingLaramieCheyenne0.0439410.05012460.00.000.00.1173910.370.02.2826094.364
6654102023-06-27NCore - North Cheyenne Soccer ComplexWyomingLaramieCheyenne0.0462350.05412500.00.000.00.0916670.280.02.2833335.265
6654112023-06-28NCore - North Cheyenne Soccer ComplexWyomingLaramieCheyenne0.0465880.0557510.00.000.00.0916670.260.02.4875004.8194
6654122023-06-29NCore - North Cheyenne Soccer ComplexWyomingLaramieCheyenne0.0487650.0569540.00.000.00.0521740.180.02.0869574.184
6654132023-06-30NCore - North Cheyenne Soccer ComplexWyomingLaramieCheyenne0.0483330.0517470.00.000.00.0208330.2110.03.20000014.8313

Duplicate rows

Most frequently occurring

DateAddressStateCountyCityO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQICO MeanCO 1st Max ValueCO 1st Max HourCO AQISO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQINO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQI# duplicates
02003-07-061130 EASTWAY DRIVENorth CarolinaMecklenburgCharlotte0.0194120.0258230.3000000.303.02.66666722.02331.05.00000014.023132
12004-05-171130 EASTWAY DRIVENorth CarolinaMecklenburgCharlotte0.0352350.04811440.3500000.405.02.75000016.0923.07.33333318.020172
22004-05-281130 EASTWAY DRIVENorth CarolinaMecklenburgCharlotte0.0234120.0518470.3375000.6237.02.7916678.0811.013.70833335.022332
32004-05-311130 EASTWAY DRIVENorth CarolinaMecklenburgCharlotte0.0377650.05015460.3458330.506.02.91666731.02344.05.62500017.023162
42004-06-071130 EASTWAY DRIVENorth CarolinaMecklenburgCharlotte0.0458820.05810610.4000000.405.00.0833331.051.09.33333321.06202
52004-06-141130 EASTWAY DRIVENorth CarolinaMecklenburgCharlotte0.0182000.0349310.3416670.4145.00.0454551.081.08.40909124.015232
62004-09-071130 EASTWAY DRIVENorth CarolinaMecklenburgCharlotte0.0200000.02523230.3000000.303.00.0000000.000.01.8333334.0742
72004-09-081130 EASTWAY DRIVENorth CarolinaMecklenburgCharlotte0.0174710.0238210.3000000.303.00.0000000.000.02.6250008.0782
82006-04-041130 EASTWAY DRIVENorth CarolinaMecklenburgCharlotte0.0377650.05211480.2416670.373.02.95000010.52214.014.39130435.06332
92006-04-171130 EASTWAY DRIVENorth CarolinaMecklenburgCharlotte0.0430590.05110470.2291670.373.05.38333325.5536.09.33333328.06262